Technical Field
The present disclosure relates generally to simultaneous localization and mapping, and more particularly, to robust simultaneous localization and mapping via removal of dynamic traffic participants
Introduction
The development of autonomous vehicles has progressed significantly due to the expansion in perception, motion planning and control, and/or emerging sensing technologies, among other factors. To achieve autonomous navigation, accurate localization and mapping may be needed. Autonomous vehicles may capture images and point clouds of an environment to assist in the localization and mapping. Autonomous vehicles perform simultaneous localization and mapping (SLAM) operations on the captured images and point clouds to build a map of the environment and obtain motion and trajectory/odometry data. SLAM operations may include one or more operations to extract, associate, estimate, and/or update localization and mapping. Often images and point clouds contain data indicating objects that are not needed for mapping of the environment and obtaining motion and trajectory/odometry data. The unnecessary objects may include dynamic traffic participants such as vehicles, pedestrians, cyclists, and animals. Inclusion of these objects during a SLAM operation may result in inaccurate or incorrect mapping and localization.
In view of the foregoing, there may be a need in the art for ways to more accurately implement localization and mapping for autonomous vehicles by way of identifying dynamic traffic participants and removing the dynamic participants prior to a SLAM operation. Further advantages and novel features will become apparent from the disclosure provided below.